Covid 19’s Development vs Election

The relationship of in-person voting VS mail-in voting and Covid-19 case development

The relationship of in-person voting VS mail-in voting and Covid-19 case development

We want to explore the relationship of in-person voting VS mail-in voting and Covid-19 case development.

Part 1

The overall pandemic situation before the voting period starts might lead to people’s preference of mail-in voting. This could be somewhat proved by the fact that people absence of in-person voting could register for mail ballot and fill in “Covid” as the excuse.

To understand this, we plot a trend of mail-in voting percentage in elections since 2000 to see if there is an evidence of increasing favoritism in mail-in voting versus other voting modes.

The background knowledge suggests that Trump administration and its supporters oppose mail-in voting, so we’ll also be looking at the difference of mail-in voting preference change for Democrats and Republicans respectively.

Percentage of mail-in voting in each election

There is a sharp increase in general mail-in voting percentage from 2016 to 2020, which could well be due to the Covid-19 development.

Percentage of mail-in voting by party

There is also a sharp increase from 2016 to 2020 by party. Particularly, Democrats favor the idea of mail-in voting much more than Republicans.

Data Source: https://dataverse.harvard.edu/dataverse/SPAE

Part 2

We’ll then explore if different voting mechanisms could have an impact on number of cases. How does the percentage of mail-in voting affects the increase of Covid-19 cases? Do democratic states and republican states differ in the increase of Covid-19 cases during the whole voting period, since they might have different policies and preferences regarding in-person vs mail-in voting at state government level?

Mail-in voting percentage & typical D/R state interactive plot

In the leaflet map, polygons are used to reflect the percentage of mail-in voting. Each state is categorized as a typical Democratic state or a Republican state based on the percentage of Democrats and Republicans from the 2020 SPAE, which is represented by the color of the state’s border on the map.

Data Source: Stewart, Charles, 2021, “2020 Survey of the Performance of American Elections”, https://doi.org/10.7910/DVN/FSGX7Z, Harvard Dataverse, V1, UNF:6:70KW4uouuTDT860MiPJq3A== [fileUNF]

Weighted Covid cases & typical D/R state interactive plot

In the leaflet map, polygons are used to reflect the percentage of Covid cases increase in the voting period till 7 days after election day. We have only kept data during the voting period. The election day is November 3, and the earliest voting time is 46 days before the election day. Reference from Early Voting Calendar. The end of our observation date is 7 days after the election day. Each state is categorized as a typical Democratic state or a Republican state based on the percentage of Democrats and Republicans from the 2020 SPAE, which is represented by the color of the state’s border on the map.

Data Source for Covid cases: United States CDC

Data Source for population: United States Census Bureau

Run a regression to see the relationship between mail-in voting percentage and weighted covid cases and visualize the relationship.

## [1] "Regression Result:"
## 
## Call:
## lm(formula = log(statetotalw) ~ mail, data = alldata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7131 -0.1719  0.1612  0.3351  0.7315 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  14.2730     0.1571  90.855   <2e-16 ***
## mail         -0.6944     0.3009  -2.308   0.0253 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5118 on 49 degrees of freedom
## Multiple R-squared:  0.09802,    Adjusted R-squared:  0.07961 
## F-statistic: 5.325 on 1 and 49 DF,  p-value: 0.02529

The result suggests that on average, 1 percent increase in mail-in voting proportion is associated with a 69.4 percent decrease of weighted covid cases in a state, not taking into other factors into account, and the influence is significant.

Do Large Gathering Events Increase Covid 19 Cases?

21 Trump’s Election Rallies’ Data

Date City County State Indoors. People.Counting
6/20/20 Tulsa Tulsa Oklahoma yes 6200
6/23/20 Phoenix Maricopa Arizona yes 3000
8/17/20 Mankato Blue Earth Minnesota no 500
8/17/20 Oshkosh Winnebago Wisconsin no 1000
8/18/20 Yuma Yuma Arizona no NA
8/20/20 Old Forge Lackawanna Pennsylvania no NA
8/28/20 Londonberry Rockingham New Hampshire no 1000
9/3/20 Latrobe Westmoreland Pennsylvania no 7000
9/8/20 Winston-Salem Forsyth North Carolina no 15000
9/10/20 Freeland Saginaw Michigan no 10000
9/12/20 Minden Douglas Nevada no 5000
9/13/20 Henderson Clark Nevada yes NA
9/17/20 Mosinee Marathon Wisconsin no NA
9/18/20 Bemidji Beltrami Minnesota no NA
9/19/20 Fayetteville Cumberland North Carolina no 5600
9/21/20 Swanton Lucas Ohio no NA
9/21/20 Vandalia Vandalia Ohio no 10000
9/22/20 Pittsburgh Allegheny Pennsylvania no NA
9/24/20 Jacksonville Duval Florida no 15000
9/25/20 Newport News Newport News Virginia no 700
9/26/20 Middletown Dauphin Pennsylvania no NA

Example – Spread Speed Increases

From the above graph, we could see after the rally event at 2020-09-17, the slope of line between 2020-09-12 to 2020-09-19 is steeper than 2020-09-06 to 2020-09-13. This might implies the Covid 19’s spread in Marathon(Mosinee), Wisconsin speed up after the rally.

Example – Spread Speed No Change

From the above graph, we could see after the rally event at 2020-08-17, the slope of line between 2020-08-13 to 2020-08-20’s slope is very similar with before. This might implies the election rally has no obvious effect on Blue Earth(Mankato), Minnesota.

Example – Spread Speed Slows Down

From the above graph, we could see after the rally event at 2020-09-10, the slope of line between 2020-09-06 to 2020-09-13 is flatter than before. This might implies the Covid 19’s spread slows down after the rally in Saginaw(Freeland), Michigan.

From the above graph, we could see after the rally event at 2020-08-17, the slope of line between 2020-08-13 to 2020-08-20 is flatter than before. This might implies the Covid 19’s spread slows down after the rally in Winnebago(Oshkosh),Wisconsin.

From the above graph, we could see after the rally event at 2020-06-10, the slope of line between 2020-06-20 to2020-06-27 is much steeper than before. This might implies the election rally speed up the Covid 19’s spread in Tulsa, Oklahoma.

From the above graph, we could see after the rally event at 2020-06-23, the slope of line between 2020-06-20 to 2020-06-27 is steeper than before. This might implies the election rally speed up the Covid 19’s spread in Maricopa(Phoenix), Arizona.However, the effect is not very obvious.

From the above graph, we could see after the rally event at 2020-08-18, the slope of line between 2020-08-13 to 2020-08-20 is slightly steeper than before. This might implies the Covid 19’s spread in Tulsa, Oklahoma slightly speed up after the rally.

From the above graph, we could see after the rally event at 2020-08-20, the slope of line between 2020-08-19 to 2020-08-26 is steeper than 2020-08-13 to 2020-08-20. This might implies the Covid 19’s spread in Lackawanna(Old Forge), Pennsylvania speed up after the election rally.

From the above graph, we could see after the rally event at 2020-08-28, the slope of line between 2020-08-25 to 2020-09-01 is almost the same as before. This might implies the Covid 19’s spread in Rockingham(Londonberry),New Hampshire slightly has no obvious change after the election rally.

From the above graph, we could see after the rally event at 2020-06-10,the slope of line between 2020-09-06 to 2020-09-13 is almost the same as 2020-08-25 to 2020-09-01. This might implies the Covid 19’s spread in Westmoreland(Latrobe),Pennsylvania has no obvious change after the election rally.

From the above graph, we could see after the rally event at 2020-09-08, the slope of line between 2020-08-13 to 2020-08-20 is flatter than before. This might implies the Covid 19’s spread slows down after the rally in Forsyth(Winston-Salem), North Carolina.

From the above graph, we could see after the rally event at 2020-09-12, the slope of line between 2020-09-12 to 2020-09-19 is steeper than 2020-09-06 to 2020-09-13. This might implies the Covid 19’s spread in Douglas(Minden), Nevada speed up after the rally.

From the above graph, we could see after the rally event at 2020-09-13, the slope of line between 2020-09-12 to 2020-09-19 is almost the same as 2020-09-06 to 2020-09-13. This might implies the Covid 19’s spread in Clark(Henderson), Nevada has no obvious change after the election rally.

From the above graph, we could see after the rally event at 2020-09-18, the slope of line between 2020-09-18 to 2020-09-25 is almost the same as 2020-09-12 to 2020-09-19 This might implies the Covid 19’s spread in Beltrami(Bemidji), Minnesota has no obvious change after the election rally.

From the above graph, we could see after the rally event at 9/19/20, the slope of line between 2020-09-18 to 2020-09-25 is almost the same as 2020-09-12 to 2020-09-19 This might implies the Covid 19’s spread in Cumberland(Fayetteville), North Carolina has no obvious change after the election rally.

From the above graph, we could see after the rally event at 2020-09-21,the slope of line between 2020-09-18 to 2020-09-25 is almost the same as 2020-09-12 to 2020-09-19 This might implies the Covid 19’s spread in Lucas(Swanton),Ohio has no obvious change after the election rally.

From the above graph, we could see after the rally event at 2020-09-21,the slope of line between 2020-09-18 to 2020-09-25 is almost the same as 2020-09-12 to 2020-09-19 This might implies the Covid 19’s spread in Vandalia, Ohio has no obvious change after the election rally.

From the above graph, we could see after the rally event at 2020-09-22,the slope of line between 2020-09-18 to 2020-09-25 is almost the same as 2020-09-12 to 2020-09-19 This might implies the Covid 19’s spread in Allegheny(Pittsburgh), Pennsylvania has no obvious change after the election rally.

From the above graph, we could see after the rally event at 2020-09-24,the slope of line between 2020-09-24 to 2020-10-01 is almost the same as 2020-09-18 to 2020-09-25. This might implies the Covid 19’s spread in Duval(Jacksonville), Florida has no obvious change after the election rally.

From the above graph, we could see after the rally event at 2020-09-25,the slope of line between 2020-09-24 to 2020-10-01 is almost the same as 2020-09-18 to 2020-09-25. This might implies the Covid 19’s spread in Newport News, Virginia has no obvious change after the election rally.

From the above graph, we could see after the rally event at 2020-09-26,the slope of line between 2020-09-24 to 2020-10-01 is almost the same as 2020-09-18 to 2020-09-25. This might implies the Covid 19’s spread in Dauphin(Middletown), Pennsylvania has no obvious change after the election rally.

Covid Spread Speed Change Summary

Date City State Indoors. Covid.Spread.After.Rally
6/20/20 Tulsa Oklahoma yes Speed up
6/23/20 Phoenix Arizona yes Speed up
8/17/20 Mankato Minnesota no No effect
8/17/20 Oshkosh Wisconsin no Slow down
8/18/20 Yuma Arizona no Speed up
8/20/20 Old Forge Pennsylvania no Speed up
8/28/20 Londonberry New Hampshire no No effect
9/3/20 Latrobe Pennsylvania no No effect
9/8/20 Winston-Salem North Carolina no Slow down
9/10/20 Freeland Michigan no Slow down
9/12/20 Minden Nevada no Speed up
9/13/20 Henderson Nevada yes Speed up
9/17/20 Mosinee Wisconsin no Speed up
9/18/20 Bemidji Minnesota no Speed up
9/19/20 Fayetteville North Carolina no No effect
9/21/20 Swanton Ohio no No effect
9/21/20 Vandalia Ohio no No effect
9/22/20 Pittsburgh Pennsylvania no Slow down
9/24/20 Jacksonville Florida no No effect
9/25/20 Newport News Virginia no No effect
9/26/20 Middletown Pennsylvania no No effect

From the above table we Could see, among all the rallies, only 38.1%(8/21) cities might have increased Covid 19 spread speed. Thus, it is hard to conclude that rallies have negative effect on Covid 19 spread.





Does Indoor or Outdoor matter?



From the above graph we could see after all indoors’ rally, the Covid 19’s spread speed increase. However, the among the outdoor rallies, the situation is much better that more than 75% of cities’ Covid 19 spread speed remain the same or even slow down.

The Relationship between Governors’ tweets and COVID-19 Cases by State

The Relationship between Governors’ tweets and COVID-19 Cases by State

We want to explore the sentiment of governors’ tweets about COVID-19 and Election in 2020. We also comapared the sentiment score and COVID-19 cases by state to see whether there’s correlation between them.

PART 1: Sentiment of Governors’ Tweets about COVID and Election

In the first part, we cleaned governors’ tweets and did a bunch of visualizations to get an overview of the tweets.

Plot Most Frequent 20 Words

From the barplot above, we can see the top 20 words in governors’ tweets. We can see that words like Covid, mask, vaccine and virus get the most frequencies. We can also see that words like test, vote, work and spread are pretty frequent as well.

Plot positive & negative word clouds by party

Plot Democratic Positive & Negative Words

From the comparison wordcloud above, we can see the negative word cloud is dominated by words like virus, spread, cases, lost, etc. And positive word cloud’s got words like protect, mask, test, vaccine, etc.

Plot Republican Positive & Negative Words

The Republican comparison word cloud is pretty much the same as Democratic word cloud, which is dominated by words like virus, outbreak, etc.

PART 2: Average Sentiment Scores of Governors’ Tweets and COVID Cases

Relationship between Average Sentiment Score of Governors’ tweets about COVID19 and Confirmed COVID Cases by State

Plot Average Sentiment Score of each Governor by State

From the leaflet map above, we can see that states like Arizona, Idaho, West Virginia, and Texas have relatively positive sentiment scores. On the other hand, states like New Mexico, South Carolina, Washington and New York have relatively negative sentiment scores.

Compare the Average Sentiment Leaflet Map Above to Covid Cases Leaflet Map

Comparing the two leaflet maps above, we can’t see obvious correlation between sentiment score and Covid cases.

US State Governors’ Relationship with Biden/Trump Affect VS Covid 19’s Development

US State Governors’ Relationship with Biden/Trump Affect VS Covid 19’s Development

library("knitr")
knitr::opts_chunk$set(echo = TRUE, eval=TRUE, message=FALSE, warning = FALSE)
d=read.csv("governors_twitter.csv") #choose the "governors_twitter" file
governor_tweets <- readRDS("governor_tweets.RDS") #choose the "governor_tweets" file
library(igraph)
library(ggraph)
library(network)
library(ggnetwork)
library(statnet)
library(ggplot2)
library(dplyr)
library(tidyverse) 

Network Analysis of governors from each state who mention Biden/Trump on Twitter

Dataset: Twitter data state governors of from 2020.6 to now

1. The network of governors who mention Biden on Twitter

#Biden
mention.Biden <- unnest(governor_tweets, mentions_screen_name)%>% filter(mentions_screen_name == "JoeBiden")
mention_Biden <- subset(mention.Biden, select=c(screen_name, mentions_screen_name))
d.Biden <- mention_Biden %>% left_join(., d, by=c('screen_name'='twitter_handle'))

g1 <- graph_from_data_frame(d.Biden , directed = TRUE)

weight<-table(d.Biden$mentions_screen_name)
degree=igraph::degree(g1, mode = 'all')

plot(g1, 
     edge.color = "grey",
     edge.size = sqrt(weight+1),
     vertex.color = ifelse( d.Biden$party == "D" , 
                     "blue", "red"),
     vertex.size = sqrt(degree+1),
     edge.arrow.size = 0.05, 
     layout = layout_nicely(g1),
     main = "The network of governors who mention Biden on Twitter")

In the graph above, the node size is determined by degree centrality, the node color is determined by parties (blue for Democrats and red for Republicans), the edge size is determined by the number of mentions between two nodes. We can learn that GovPritzker and GavinNewsom have mentioned Biden the most times on Twitter. In other words, the relationship strength between them is strong. Most governors mentioned Biden on Twitter are Democrats, which are in the same party with Biden.

2. The network of governors who mention Trump

#Trump
mention.Trump <- unnest(governor_tweets, mentions_screen_name)%>% filter(mentions_screen_name == "realDonaldTrump")

mention_Trump <- subset(mention.Trump, select=c(screen_name, mentions_screen_name))
d.Trump <- mention_Trump %>% left_join(., d, by=c('screen_name'='twitter_handle'))
g2 <- graph_from_data_frame(d.Trump , directed = TRUE)

weight<-table(d.Trump$mentions_screen_name)
degree=igraph::degree(g2, mode = 'all')

plot(g2, 
     edge.color = "grey",
     edge.size = sqrt(weight+1),
     vertex.color = ifelse( d.Trump$party == "D" , 
                     "blue", "red"),
     vertex.size = sqrt(degree+1), 
     edge.arrow.size = 0.05, 
     layout = layout_nicely(g2),
     main = "The network of governors who mention Trump on Twitter")

In the graph above, the node size is determined by degree centrality, the node color is determined by parties (blue for Democrats and red for Republicans), the edge size is determined by the number of mentions between two nodes. We can learn that GovDunleavy, BrainKempGA and KimReynoldsIA have mentioned Trump the most times on Twitter. In other words, the relationship strength between them is strong. Almost all governors mentioned Trump on Twitter are Republicans, which are in the same party with Trump. Compared with Biden, Trump is mentioned more by governors.